Patient-Perceived Impact of the COVID-19 Pandemic on Medication Adherence and Access to Care for Long-Term Diseases: A Cross-Sectional Online Survey
Bibliographic record
Abstract
The COVID-19 pandemic has been associated with lifestyle changes, reduced access to care and potential impacts on medication self-management. Our main objectives are to evaluate the impact of the pandemic on patient adherence and access to care and long-term medications and determine its association with sociodemographic and clinical factors. This study is part of the Specchio-COVID-19 longitudinal cohort study in Geneva, Switzerland, conducted through an online questionnaire. Among the 982 participants (median age: 56; 61% female), 827 took long-term medications. There were 76 reported changes in medication dosages, of which 24 (31%) were without a physician’s recommendation, and 51 delays in initiation or premature medication interruptions, of which 24 (47%) were without a physician’s recommendation. Only 1% (9/827) of participants faced medication access issues. Participants taking a respiratory medication had a four-times greater odds of reporting more regular medication (OR = 4.27; CI 95%: 2.11–8.63) intake, whereas each year increase in age was significantly associated with 6% fewer relative risks of discontinuation (OR = 0.94; CI 95%: 0.91–0.97) and 3% fewer relative risks of changes in medication dosage (OR = 0.97; CI 95%: 0.95–1.00). Despite the limited impact of the pandemic on adherence and access to medications, our results emphasize the need for understanding patient challenges when self-managing their long-term medication, notably during public health crises.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".